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Installing collected packages: pip
  Attempting uninstall: pip
    Found existing installation: pip 23.3.2
    Uninstalling pip-23.3.2:
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Successfully installed pip-24.0
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Collecting mxnet<2.0.0
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Collecting bokeh==2.0.1
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Installing collected packages: graphviz, mxnet, bokeh
  Attempting uninstall: graphviz
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    Uninstalling graphviz-0.20.3:
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Collecting hyperopt<0.2.8,>=0.2.7 (from autogluon.core[all]==0.8.2->autogluon)
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Collecting ray<2.7,>=2.6.3 (from ray[tune]<2.7,>=2.6.3; extra == "all"->autogluon.core[all]==0.8.2->autogluon)
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Requirement already satisfied: Pillow<9.6,>=9.3 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (9.5.0)
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Collecting py4j (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==0.8.2->autogluon)
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Building wheels for collected packages: gpustat
  Building wheel for gpustat (pyproject.toml) ... done
  Created wheel for gpustat: filename=gpustat-1.1.1-py3-none-any.whl size=26532 sha256=1f24db2b5b2a195daed7549b1dafb1a14eaf220729e499a5e3d6250087ff3578
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Successfully built gpustat
Installing collected packages: py4j, py-spy, opencensus-context, nvidia-ml-py, distlib, colorful, tensorboardX, proto-plus, platformdirs, googleapis-common-protos, blessed, virtualenv, ray, hyperopt, gpustat, google-api-core, aiohttp-cors, opencensus
  Attempting uninstall: platformdirs
    Found existing installation: platformdirs 4.2.0
    Uninstalling platformdirs-4.2.0:
      Successfully uninstalled platformdirs-4.2.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
sparkmagic 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.4 which is incompatible.
Successfully installed aiohttp-cors-0.7.0 blessed-1.20.0 colorful-0.5.6 distlib-0.3.8 google-api-core-2.18.0 googleapis-common-protos-1.63.0 gpustat-1.1.1 hyperopt-0.2.7 nvidia-ml-py-12.550.52 opencensus-0.11.4 opencensus-context-0.1.3 platformdirs-3.11.0 proto-plus-1.23.0 py-spy-0.3.14 py4j-0.10.9.7 ray-2.6.3 tensorboardX-2.6.2.2 virtualenv-20.21.0
In [18]:
!echo '{"username":"USERNAME","key":"KEY"}' > ~/.kaggle/kaggle.json
In [19]:
!mkdir -p .kaggle
!touch .kaggle/kaggle.json
!chmod 600 .kaggle/kaggle.json
In [20]:
import json
kaggle_username = "divyachauhan3301"
kaggle_key = "eb781f878f360e585e1aeb9e7828a87d"

# Save API token the kaggle.json file
with open(".kaggle/kaggle.json", "w") as f:
    f.write(json.dumps({"username": kaggle_username, "key": kaggle_key}))
In [21]:
import pandas as pd
from autogluon.tabular import TabularPredictor
In [ ]:
 
In [22]:
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
submission = pd.read_csv('sampleSubmission.csv')
In [23]:
train.head()
Out[23]:
datetime season holiday workingday weather temp atemp humidity windspeed casual registered count
0 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 81 0.0 3 13 16
1 2011-01-01 01:00:00 1 0 0 1 9.02 13.635 80 0.0 8 32 40
2 2011-01-01 02:00:00 1 0 0 1 9.02 13.635 80 0.0 5 27 32
3 2011-01-01 03:00:00 1 0 0 1 9.84 14.395 75 0.0 3 10 13
4 2011-01-01 04:00:00 1 0 0 1 9.84 14.395 75 0.0 0 1 1
In [24]:
test.head()
Out[24]:
datetime season holiday workingday weather temp atemp humidity windspeed
0 2011-01-20 00:00:00 1 0 1 1 10.66 11.365 56 26.0027
1 2011-01-20 01:00:00 1 0 1 1 10.66 13.635 56 0.0000
2 2011-01-20 02:00:00 1 0 1 1 10.66 13.635 56 0.0000
3 2011-01-20 03:00:00 1 0 1 1 10.66 12.880 56 11.0014
4 2011-01-20 04:00:00 1 0 1 1 10.66 12.880 56 11.0014
In [25]:
submission.head()
Out[25]:
datetime count
0 2011-01-20 00:00:00 0
1 2011-01-20 01:00:00 0
2 2011-01-20 02:00:00 0
3 2011-01-20 03:00:00 0
4 2011-01-20 04:00:00 0
In [ ]:
predictor = TabularPredictor(
    label="count", problem_type="regression", eval_metric="rmse"
    ).fit(
    train_data=train.drop(['casual', 'registered'], axis=1),
    time_limit=600,
    presets='best_quality')
No path specified. Models will be saved in: "AutogluonModels/ag-20240430_032935"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20240430_032935"
AutoGluon Version:  0.8.2
Python Version:     3.10.14
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #1 SMP Sat Mar 23 09:49:55 UTC 2024
Disk Space Avail:   5.18 GB / 5.36 GB (96.7%)
	WARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception. 
	We recommend a minimum available disk space of 10 GB, and large datasets may require more.
Train Data Rows:    10886
Train Data Columns: 9
Label Column: count
Preprocessing data ...
/opt/conda/lib/python3.10/site-packages/autogluon/tabular/learner/default_learner.py:215: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context("mode.use_inf_as_na", True):  # treat None, NaN, INF, NINF as NA
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    2195.09 MB
	Train Data (Original)  Memory Usage: 1.52 MB (0.1% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
/opt/conda/lib/python3.10/site-packages/autogluon/features/generators/fillna.py:58: FutureWarning: The 'downcast' keyword in fillna is deprecated and will be removed in a future version. Use res.infer_objects(copy=False) to infer non-object dtype, or pd.to_numeric with the 'downcast' keyword to downcast numeric results.
  X.fillna(self._fillna_feature_map, inplace=True, downcast=False)
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Stage 5 Generators:
		Fitting DropDuplicatesFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('float', [])                      : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                        : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']
		('object', ['datetime_as_object']) : 1 | ['datetime']
	Types of features in processed data (raw dtype, special dtypes):
		('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                  : 3 | ['season', 'weather', 'humidity']
		('int', ['bool'])            : 2 | ['holiday', 'workingday']
		('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
	0.1s = Fit runtime
	9 features in original data used to generate 13 features in processed data.
	Train Data (Processed) Memory Usage: 0.98 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.18s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
	To change this, specify the eval_metric parameter of Predictor()
User-specified model hyperparameters to be fit:
{
	'NN_TORCH': {},
	'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],
	'CAT': {},
	'XGB': {},
	'FASTAI': {},
	'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
	'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
	'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
}
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.78s of the 599.81s of remaining time.
	-101.5462	 = Validation score   (-root_mean_squared_error)
	0.04s	 = Training   runtime
	0.05s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 396.75s of the 596.78s of remaining time.
	-84.1251	 = Validation score   (-root_mean_squared_error)
	0.03s	 = Training   runtime
	0.07s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 396.61s of the 596.65s of remaining time.
Will use sequential fold fitting strategy because import of ray failed. Reason: ray is required to train folds in parallel for TabularPredictor or HPO for MultiModalPredictor. A quick tip is to install via `pip install ray==2.6.3`
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
/opt/conda/lib/python3.10/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.
  warnings.warn(
/opt/conda/lib/python3.10/site-packages/dask/dataframe/__init__.py:31: FutureWarning: 
Dask dataframe query planning is disabled because dask-expr is not installed.

You can install it with `pip install dask[dataframe]` or `conda install dask`.
This will raise in a future version.

  warnings.warn(msg, FutureWarning)
[1000]	valid_set's rmse: 131.684
[2000]	valid_set's rmse: 130.67
[3000]	valid_set's rmse: 130.626
[1000]	valid_set's rmse: 135.592
[1000]	valid_set's rmse: 133.481
[2000]	valid_set's rmse: 132.323
[3000]	valid_set's rmse: 131.618
[4000]	valid_set's rmse: 131.443
[5000]	valid_set's rmse: 131.265
[6000]	valid_set's rmse: 131.277
[7000]	valid_set's rmse: 131.443
[1000]	valid_set's rmse: 128.503
[2000]	valid_set's rmse: 127.654
[3000]	valid_set's rmse: 127.227
[4000]	valid_set's rmse: 127.105
[1000]	valid_set's rmse: 134.135
[2000]	valid_set's rmse: 132.272
[3000]	valid_set's rmse: 131.286
[4000]	valid_set's rmse: 130.752
[5000]	valid_set's rmse: 130.363
[6000]	valid_set's rmse: 130.509
[1000]	valid_set's rmse: 136.168
[2000]	valid_set's rmse: 135.138
[3000]	valid_set's rmse: 135.029
[1000]	valid_set's rmse: 134.061
[2000]	valid_set's rmse: 133.034
[3000]	valid_set's rmse: 132.182
[4000]	valid_set's rmse: 131.997
[5000]	valid_set's rmse: 131.643
[6000]	valid_set's rmse: 131.504
[7000]	valid_set's rmse: 131.574
[1000]	valid_set's rmse: 132.912
[2000]	valid_set's rmse: 131.703
[3000]	valid_set's rmse: 131.117
[4000]	valid_set's rmse: 130.82
[5000]	valid_set's rmse: 130.673
[6000]	valid_set's rmse: 130.708
	-131.4609	 = Validation score   (-root_mean_squared_error)
	48.55s	 = Training   runtime
	6.38s	 = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 335.3s of the 535.33s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
[1000]	valid_set's rmse: 130.818
In [29]:
predictor.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
                     model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0      WeightedEnsemble_L3  -52.743784      11.395770  525.655306                0.000636           0.279644            3       True         16
1   RandomForestMSE_BAG_L2  -53.391974      10.371316  417.029261                0.773592          39.790365            2       True         13
2     ExtraTreesMSE_BAG_L2  -53.746435      10.315211  388.384858                0.717486          11.145962            2       True         15
3          LightGBM_BAG_L2  -55.043589       9.857359  389.048964                0.259634          11.810067            2       True         12
4          CatBoost_BAG_L2  -55.358038       9.644422  462.629268                0.046697          85.390371            2       True         14
5        LightGBMXT_BAG_L2  -59.788370      12.972428  422.900408                3.374703          45.661511            2       True         11
6    KNeighborsDist_BAG_L1  -84.125061       0.068755    0.030827                0.068755           0.030827            1       True          2
7      WeightedEnsemble_L2  -84.125061       0.069344    0.411616                0.000590           0.380789            2       True         10
8    KNeighborsUnif_BAG_L1 -101.546199       0.045192    0.035783                0.045192           0.035783            1       True          1
9   RandomForestMSE_BAG_L1 -116.548359       0.629014   14.247486                0.629014          14.247486            1       True          5
10    ExtraTreesMSE_BAG_L1 -124.600676       0.612904    7.997195                0.612904           7.997195            1       True          7
11         CatBoost_BAG_L1 -130.498580       0.073415  232.885981                0.073415         232.885981            1       True          6
12         LightGBM_BAG_L1 -131.054162       1.411598   12.648717                1.411598          12.648717            1       True          4
13       LightGBMXT_BAG_L1 -131.460909       6.384120   48.548435                6.384120          48.548435            1       True          3
14          XGBoost_BAG_L1 -132.487303       0.134549    3.372401                0.134549           3.372401            1       True          9
15  NeuralNetFastAI_BAG_L1 -137.973239       0.238179   57.472072                0.238179          57.472072            1       True          8
Number of models trained: 16
Types of models trained:
{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_XGBoost', 'WeightedEnsembleModel', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_RF'}
Bagging used: True  (with 8 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
('int', [])                  : 3 | ['season', 'weather', 'humidity']
('int', ['bool'])            : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
*** End of fit() summary ***
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"
  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"')
Out[29]:
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
  'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',
  'XGBoost_BAG_L1': 'StackerEnsembleModel_XGBoost',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
  'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
  'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,
  'KNeighborsDist_BAG_L1': -84.12506123181602,
  'LightGBMXT_BAG_L1': -131.46090891834504,
  'LightGBM_BAG_L1': -131.054161598899,
  'RandomForestMSE_BAG_L1': -116.54835939455667,
  'CatBoost_BAG_L1': -130.49858036848312,
  'ExtraTreesMSE_BAG_L1': -124.60067564699747,
  'NeuralNetFastAI_BAG_L1': -137.97323934290566,
  'XGBoost_BAG_L1': -132.4873031302656,
  'WeightedEnsemble_L2': -84.12506123181602,
  'LightGBMXT_BAG_L2': -59.78836974728784,
  'LightGBM_BAG_L2': -55.04358945978129,
  'RandomForestMSE_BAG_L2': -53.391974165298606,
  'CatBoost_BAG_L2': -55.358038111004205,
  'ExtraTreesMSE_BAG_L2': -53.74643451738374,
  'WeightedEnsemble_L3': -52.74378370945104},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'KNeighborsUnif_BAG_L1': ['KNeighborsUnif_BAG_L1'],
  'KNeighborsDist_BAG_L1': ['KNeighborsDist_BAG_L1'],
  'LightGBMXT_BAG_L1': ['LightGBMXT_BAG_L1'],
  'LightGBM_BAG_L1': ['LightGBM_BAG_L1'],
  'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],
  'CatBoost_BAG_L1': ['CatBoost_BAG_L1'],
  'ExtraTreesMSE_BAG_L1': ['ExtraTreesMSE_BAG_L1'],
  'NeuralNetFastAI_BAG_L1': ['NeuralNetFastAI_BAG_L1'],
  'XGBoost_BAG_L1': ['XGBoost_BAG_L1'],
  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],
  'LightGBMXT_BAG_L2': ['LightGBMXT_BAG_L2'],
  'LightGBM_BAG_L2': ['LightGBM_BAG_L2'],
  'RandomForestMSE_BAG_L2': ['RandomForestMSE_BAG_L2'],
  'CatBoost_BAG_L2': ['CatBoost_BAG_L2'],
  'ExtraTreesMSE_BAG_L2': ['ExtraTreesMSE_BAG_L2'],
  'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},
 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.035782814025878906,
  'KNeighborsDist_BAG_L1': 0.030827045440673828,
  'LightGBMXT_BAG_L1': 48.54843473434448,
  'LightGBM_BAG_L1': 12.648716926574707,
  'RandomForestMSE_BAG_L1': 14.247486352920532,
  'CatBoost_BAG_L1': 232.88598132133484,
  'ExtraTreesMSE_BAG_L1': 7.997194766998291,
  'NeuralNetFastAI_BAG_L1': 57.4720721244812,
  'XGBoost_BAG_L1': 3.3724005222320557,
  'WeightedEnsemble_L2': 0.38078927993774414,
  'LightGBMXT_BAG_L2': 45.66151142120361,
  'LightGBM_BAG_L2': 11.810067176818848,
  'RandomForestMSE_BAG_L2': 39.790364503860474,
  'CatBoost_BAG_L2': 85.39037132263184,
  'ExtraTreesMSE_BAG_L2': 11.14596152305603,
  'WeightedEnsemble_L3': 0.2796444892883301},
 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.04519152641296387,
  'KNeighborsDist_BAG_L1': 0.06875467300415039,
  'LightGBMXT_BAG_L1': 6.384119749069214,
  'LightGBM_BAG_L1': 1.4115982055664062,
  'RandomForestMSE_BAG_L1': 0.629014253616333,
  'CatBoost_BAG_L1': 0.07341456413269043,
  'ExtraTreesMSE_BAG_L1': 0.6129038333892822,
  'NeuralNetFastAI_BAG_L1': 0.23817920684814453,
  'XGBoost_BAG_L1': 0.13454890251159668,
  'WeightedEnsemble_L2': 0.0005896091461181641,
  'LightGBMXT_BAG_L2': 3.3747026920318604,
  'LightGBM_BAG_L2': 0.25963425636291504,
  'RandomForestMSE_BAG_L2': 0.7735915184020996,
  'CatBoost_BAG_L2': 0.04669690132141113,
  'ExtraTreesMSE_BAG_L2': 0.7174859046936035,
  'WeightedEnsemble_L3': 0.0006363391876220703},
 'num_bag_folds': 8,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBMXT_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'CatBoost_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'XGBoost_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBMXT_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'CatBoost_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'ExtraTreesMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                      model   score_val  pred_time_val    fit_time  \
 0      WeightedEnsemble_L3  -52.743784      11.395770  525.655306   
 1   RandomForestMSE_BAG_L2  -53.391974      10.371316  417.029261   
 2     ExtraTreesMSE_BAG_L2  -53.746435      10.315211  388.384858   
 3          LightGBM_BAG_L2  -55.043589       9.857359  389.048964   
 4          CatBoost_BAG_L2  -55.358038       9.644422  462.629268   
 5        LightGBMXT_BAG_L2  -59.788370      12.972428  422.900408   
 6    KNeighborsDist_BAG_L1  -84.125061       0.068755    0.030827   
 7      WeightedEnsemble_L2  -84.125061       0.069344    0.411616   
 8    KNeighborsUnif_BAG_L1 -101.546199       0.045192    0.035783   
 9   RandomForestMSE_BAG_L1 -116.548359       0.629014   14.247486   
 10    ExtraTreesMSE_BAG_L1 -124.600676       0.612904    7.997195   
 11         CatBoost_BAG_L1 -130.498580       0.073415  232.885981   
 12         LightGBM_BAG_L1 -131.054162       1.411598   12.648717   
 13       LightGBMXT_BAG_L1 -131.460909       6.384120   48.548435   
 14          XGBoost_BAG_L1 -132.487303       0.134549    3.372401   
 15  NeuralNetFastAI_BAG_L1 -137.973239       0.238179   57.472072   
 
     pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                 0.000636           0.279644            3       True   
 1                 0.773592          39.790365            2       True   
 2                 0.717486          11.145962            2       True   
 3                 0.259634          11.810067            2       True   
 4                 0.046697          85.390371            2       True   
 5                 3.374703          45.661511            2       True   
 6                 0.068755           0.030827            1       True   
 7                 0.000590           0.380789            2       True   
 8                 0.045192           0.035783            1       True   
 9                 0.629014          14.247486            1       True   
 10                0.612904           7.997195            1       True   
 11                0.073415         232.885981            1       True   
 12                1.411598          12.648717            1       True   
 13                6.384120          48.548435            1       True   
 14                0.134549           3.372401            1       True   
 15                0.238179          57.472072            1       True   
 
     fit_order  
 0          16  
 1          13  
 2          15  
 3          12  
 4          14  
 5          11  
 6           2  
 7          10  
 8           1  
 9           5  
 10          7  
 11          6  
 12          4  
 13          3  
 14          9  
 15          8  }
In [30]:
predictions = predictor.predict(test)
predictions = {'datetime': test['datetime'], 'Pred_count': predictions}
predictions = pd.DataFrame(data=predictions)
predictions.head()
/opt/conda/lib/python3.10/site-packages/autogluon/features/generators/fillna.py:58: FutureWarning: The 'downcast' keyword in fillna is deprecated and will be removed in a future version. Use res.infer_objects(copy=False) to infer non-object dtype, or pd.to_numeric with the 'downcast' keyword to downcast numeric results.
  X.fillna(self._fillna_feature_map, inplace=True, downcast=False)
Out[30]:
datetime Pred_count
0 2011-01-20 00:00:00 23.875008
1 2011-01-20 01:00:00 41.514889
2 2011-01-20 02:00:00 46.342697
3 2011-01-20 03:00:00 49.907799
4 2011-01-20 04:00:00 52.934887
In [ ]:
 
In [31]:
predictions.describe()
Out[31]:
Pred_count
count 6493.000000
mean 101.133789
std 90.489120
min 2.932264
25% 21.513674
50% 62.904556
75% 171.659409
max 365.803619
In [32]:
negative = predictions.groupby(predictions['Pred_count'])

# lambda function
def minus(val):
   return val[val < 0].sum()

print(negative['Pred_count'].agg([('negcount', minus)]))
            negcount
Pred_count          
2.932264         0.0
2.978237         0.0
3.043153         0.0
3.071162         0.0
3.097124         0.0
...              ...
363.410614       0.0
365.069702       0.0
365.383972       0.0
365.449768       0.0
365.803619       0.0

[6248 rows x 1 columns]
In [33]:
predictions[predictions['Pred_count']<0] = 0
In [34]:
predictions.describe()
Out[34]:
Pred_count
count 6493.000000
mean 101.133789
std 90.489120
min 2.932264
25% 21.513674
50% 62.904556
75% 171.659409
max 365.803619
In [35]:
predictions.head()
Out[35]:
datetime Pred_count
0 2011-01-20 00:00:00 23.875008
1 2011-01-20 01:00:00 41.514889
2 2011-01-20 02:00:00 46.342697
3 2011-01-20 03:00:00 49.907799
4 2011-01-20 04:00:00 52.934887
In [36]:
submission["count"] = predictions['Pred_count']
submission.to_csv("submission.csv", index=False)
In [43]:
import kaggle
In [44]:
!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "first raw submission"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 665kB/s]
Successfully submitted to Bike Sharing Demand
In [45]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName                     date                 description                        status    publicScore  privateScore  
---------------------------  -------------------  ---------------------------------  --------  -----------  ------------  
submission.csv               2024-04-30 03:42:29  first raw submission               pending                              
submission_new_hpo.csv       2024-04-30 02:53:22  new features with hyperparameters  complete  0.48188      0.48188       
submission_new_features.csv  2024-04-30 02:37:04  new features                       complete  0.6741       0.6741        
submission.csv               2024-04-30 02:20:52  first raw submission               complete  1.80512      1.80512       
In [46]:
train.hist()
Out[46]:
array([[<Axes: title={'center': 'datetime'}>,
        <Axes: title={'center': 'season'}>,
        <Axes: title={'center': 'holiday'}>,
        <Axes: title={'center': 'workingday'}>],
       [<Axes: title={'center': 'weather'}>,
        <Axes: title={'center': 'temp'}>,
        <Axes: title={'center': 'atemp'}>,
        <Axes: title={'center': 'humidity'}>],
       [<Axes: title={'center': 'windspeed'}>,
        <Axes: title={'center': 'casual'}>,
        <Axes: title={'center': 'registered'}>,
        <Axes: title={'center': 'count'}>],
       [<Axes: title={'center': 'year'}>,
        <Axes: title={'center': 'month'}>,
        <Axes: title={'center': 'day'}>,
        <Axes: title={'center': 'hour'}>]], dtype=object)
No description has been provided for this image
In [47]:
train['datetime'] = pd.to_datetime(train['datetime'])
test['datetime'] = pd.to_datetime(test['datetime'])

# Access year, month, and day
train['year'] = train['datetime'].dt.year
train['month'] = train['datetime'].dt.month
train['day'] = train['datetime'].dt.day
train['hour'] = train['datetime'].dt.hour

test['year'] = test['datetime'].dt.year
test['month'] = test['datetime'].dt.month
test['day'] = test['datetime'].dt.day
test['hour'] = test['datetime'].dt.hour
In [48]:
train["season"] = train["season"].astype("category")
train["weather"] = train["weather"].astype("category")
test["season"] = test["season"].astype("category")
test["weather"] = test["weather"].astype("category")
In [49]:
train.head()
Out[49]:
datetime season holiday workingday weather temp atemp humidity windspeed casual registered count year month day hour
0 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 81 0.0 3 13 16 2011 1 1 0
1 2011-01-01 01:00:00 1 0 0 1 9.02 13.635 80 0.0 8 32 40 2011 1 1 1
2 2011-01-01 02:00:00 1 0 0 1 9.02 13.635 80 0.0 5 27 32 2011 1 1 2
3 2011-01-01 03:00:00 1 0 0 1 9.84 14.395 75 0.0 3 10 13 2011 1 1 3
4 2011-01-01 04:00:00 1 0 0 1 9.84 14.395 75 0.0 0 1 1 2011 1 1 4
In [50]:
train.hist()
Out[50]:
array([[<Axes: title={'center': 'datetime'}>,
        <Axes: title={'center': 'holiday'}>,
        <Axes: title={'center': 'workingday'}>,
        <Axes: title={'center': 'temp'}>],
       [<Axes: title={'center': 'atemp'}>,
        <Axes: title={'center': 'humidity'}>,
        <Axes: title={'center': 'windspeed'}>,
        <Axes: title={'center': 'casual'}>],
       [<Axes: title={'center': 'registered'}>,
        <Axes: title={'center': 'count'}>,
        <Axes: title={'center': 'year'}>,
        <Axes: title={'center': 'month'}>],
       [<Axes: title={'center': 'day'}>,
        <Axes: title={'center': 'hour'}>, <Axes: >, <Axes: >]],
      dtype=object)
No description has been provided for this image
In [51]:
predictor_new_features = TabularPredictor(
    label="count", problem_type="regression", eval_metric="rmse"
    ).fit(
    train_data=train.drop(['casual', 'registered'], axis=1),
    time_limit=600,
    presets='best_quality')
No path specified. Models will be saved in: "AutogluonModels/ag-20240430_034236"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20240430_034236"
AutoGluon Version:  0.8.2
Python Version:     3.10.14
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #1 SMP Sat Mar 23 09:49:55 UTC 2024
Disk Space Avail:   3.81 GB / 5.36 GB (71.1%)
	WARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception. 
	We recommend a minimum available disk space of 10 GB, and large datasets may require more.
Train Data Rows:    10886
Train Data Columns: 13
Label Column: count
Preprocessing data ...
/opt/conda/lib/python3.10/site-packages/autogluon/tabular/learner/default_learner.py:215: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context("mode.use_inf_as_na", True):  # treat None, NaN, INF, NINF as NA
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    2131.48 MB
	Train Data (Original)  Memory Usage: 0.81 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Stage 5 Generators:
		Fitting DropDuplicatesFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) : 2 | ['season', 'weather']
		('datetime', []) : 1 | ['datetime']
		('float', [])    : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])      : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             : 2 | ['season', 'weather']
		('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                  : 4 | ['humidity', 'month', 'day', 'hour']
		('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']
		('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']
	0.9s = Fit runtime
	13 features in original data used to generate 15 features in processed data.
	Train Data (Processed) Memory Usage: 0.8 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.98s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
	To change this, specify the eval_metric parameter of Predictor()
User-specified model hyperparameters to be fit:
{
	'NN_TORCH': {},
	'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],
	'CAT': {},
	'XGB': {},
	'FASTAI': {},
	'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
	'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
	'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
}
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.24s of the 599.01s of remaining time.
	-101.5462	 = Validation score   (-root_mean_squared_error)
	0.06s	 = Training   runtime
	0.06s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.06s of the 598.83s of remaining time.
	-84.1251	 = Validation score   (-root_mean_squared_error)
	0.03s	 = Training   runtime
	0.05s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 398.94s of the 598.71s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
[1000]	valid_set's rmse: 35.722
[2000]	valid_set's rmse: 34.0646
[3000]	valid_set's rmse: 33.7501
[4000]	valid_set's rmse: 33.5663
[5000]	valid_set's rmse: 33.5927
[1000]	valid_set's rmse: 36.6943
[2000]	valid_set's rmse: 34.7009
[3000]	valid_set's rmse: 34.2654
[4000]	valid_set's rmse: 34.0805
[5000]	valid_set's rmse: 34.0068
[6000]	valid_set's rmse: 33.9926
[7000]	valid_set's rmse: 34.0148
[8000]	valid_set's rmse: 34.0505
[1000]	valid_set's rmse: 37.0225
[2000]	valid_set's rmse: 34.5264
[3000]	valid_set's rmse: 33.9428
[4000]	valid_set's rmse: 33.6752
[5000]	valid_set's rmse: 33.5411
[6000]	valid_set's rmse: 33.4628
[7000]	valid_set's rmse: 33.3908
[8000]	valid_set's rmse: 33.3862
[9000]	valid_set's rmse: 33.3645
[10000]	valid_set's rmse: 33.3686
[1000]	valid_set's rmse: 38.1752
[2000]	valid_set's rmse: 36.5188
[3000]	valid_set's rmse: 36.1264
[4000]	valid_set's rmse: 35.9954
[5000]	valid_set's rmse: 35.9337
[6000]	valid_set's rmse: 35.9463
[1000]	valid_set's rmse: 38.9031
[2000]	valid_set's rmse: 36.7896
[3000]	valid_set's rmse: 36.3287
[4000]	valid_set's rmse: 36.2175
[5000]	valid_set's rmse: 36.1359
[6000]	valid_set's rmse: 36.0948
[7000]	valid_set's rmse: 36.174
[1000]	valid_set's rmse: 35.8977
[2000]	valid_set's rmse: 33.4992
[3000]	valid_set's rmse: 32.7907
[4000]	valid_set's rmse: 32.4471
[5000]	valid_set's rmse: 32.2892
[6000]	valid_set's rmse: 32.2846
[7000]	valid_set's rmse: 32.2649
[8000]	valid_set's rmse: 32.3084
[1000]	valid_set's rmse: 38.3394
[2000]	valid_set's rmse: 37.1199
[3000]	valid_set's rmse: 36.8417
[4000]	valid_set's rmse: 36.6798
[5000]	valid_set's rmse: 36.6466
[6000]	valid_set's rmse: 36.6288
[7000]	valid_set's rmse: 36.6832
[1000]	valid_set's rmse: 35.8969
[2000]	valid_set's rmse: 34.1606
[3000]	valid_set's rmse: 33.8527
[4000]	valid_set's rmse: 33.714
[5000]	valid_set's rmse: 33.6917
	-34.4539	 = Validation score   (-root_mean_squared_error)
	73.51s	 = Training   runtime
	11.95s	 = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 302.83s of the 502.6s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
[1000]	valid_set's rmse: 33.1713
[2000]	valid_set's rmse: 33.0077
[1000]	valid_set's rmse: 32.8635
[2000]	valid_set's rmse: 32.6404
[1000]	valid_set's rmse: 31.9543
[2000]	valid_set's rmse: 31.343
[3000]	valid_set's rmse: 30.9039
[4000]	valid_set's rmse: 30.8612
[1000]	valid_set's rmse: 35.8483
[2000]	valid_set's rmse: 35.4773
[3000]	valid_set's rmse: 35.3993
[1000]	valid_set's rmse: 35.5388
[1000]	valid_set's rmse: 31.6283
[1000]	valid_set's rmse: 37.9327
[2000]	valid_set's rmse: 37.4577
[1000]	valid_set's rmse: 34.9434
[2000]	valid_set's rmse: 34.6719
	-33.9173	 = Validation score   (-root_mean_squared_error)
	25.25s	 = Training   runtime
	3.05s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 271.09s of the 470.86s of remaining time.
	-38.425	 = Validation score   (-root_mean_squared_error)
	16.73s	 = Training   runtime
	0.57s	 = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 253.37s of the 453.14s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Ran out of time, early stopping on iteration 2589.
	Ran out of time, early stopping on iteration 2730.
	Ran out of time, early stopping on iteration 2714.
	Ran out of time, early stopping on iteration 2814.
	Ran out of time, early stopping on iteration 3062.
	Ran out of time, early stopping on iteration 3113.
	Ran out of time, early stopping on iteration 3355.
	Ran out of time, early stopping on iteration 3689.
	-34.056	 = Validation score   (-root_mean_squared_error)
	243.0s	 = Training   runtime
	0.1s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 10.15s of the 209.91s of remaining time.
	-38.1073	 = Validation score   (-root_mean_squared_error)
	8.32s	 = Training   runtime
	0.55s	 = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 0.81s of the 200.58s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Time limit exceeded... Skipping NeuralNetFastAI_BAG_L1.
Fitting model: XGBoost_BAG_L1 ... Training model for up to 0.57s of the 200.33s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Time limit exceeded... Skipping XGBoost_BAG_L1.
Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 0.44s of the 200.21s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Time limit exceeded... Skipping NeuralNetTorch_BAG_L1.
Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 0.34s of the 200.11s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Ran out of time, early stopping on iteration 1. Best iteration is:
	[1]	valid_set's rmse: 176.729
	Time limit exceeded... Skipping LightGBMLarge_BAG_L1.
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 199.73s of remaining time.
	-32.1785	 = Validation score   (-root_mean_squared_error)
	0.39s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 199.32s of the 199.3s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
[1000]	valid_set's rmse: 30.095
[1000]	valid_set's rmse: 30.9622
	-31.1534	 = Validation score   (-root_mean_squared_error)
	15.13s	 = Training   runtime
	0.71s	 = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 182.32s of the 182.3s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	-30.6569	 = Validation score   (-root_mean_squared_error)
	10.26s	 = Training   runtime
	0.26s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 171.42s of the 171.41s of remaining time.
	-31.678	 = Validation score   (-root_mean_squared_error)
	40.14s	 = Training   runtime
	0.61s	 = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 130.24s of the 130.22s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Ran out of time, early stopping on iteration 1275.
	Ran out of time, early stopping on iteration 1242.
	-30.4785	 = Validation score   (-root_mean_squared_error)
	120.83s	 = Training   runtime
	0.06s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 9.28s of the 9.26s of remaining time.
	-31.4883	 = Validation score   (-root_mean_squared_error)
	12.32s	 = Training   runtime
	0.75s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -4.37s of remaining time.
	-30.2294	 = Validation score   (-root_mean_squared_error)
	0.32s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 604.72s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20240430_034236")
In [52]:
predictor_new_features.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
                     model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0      WeightedEnsemble_L3  -30.229371      17.975116  553.563776                0.000632           0.319943            3       True         14
1          CatBoost_BAG_L2  -30.478472      16.387324  487.721086                0.057159         120.831526            2       True         12
2          LightGBM_BAG_L2  -30.656909      16.592012  377.147769                0.261847          10.258209            2       True         10
3        LightGBMXT_BAG_L2  -31.153360      17.044061  382.018803                0.713896          15.129243            2       True          9
4     ExtraTreesMSE_BAG_L2  -31.488288      17.083068  379.207504                0.752903          12.317944            2       True         13
5   RandomForestMSE_BAG_L2  -31.678040      16.941582  407.024855                0.611417          40.135296            2       True         11
6      WeightedEnsemble_L2  -32.178548      15.721569  358.911448                0.000759           0.393991            2       True          8
7          LightGBM_BAG_L1  -33.917339       3.049416   25.245680                3.049416          25.245680            1       True          4
8          CatBoost_BAG_L1  -34.056005       0.100985  242.995549                0.100985         242.995549            1       True          6
9        LightGBMXT_BAG_L1  -34.453884      11.953281   73.513387               11.953281          73.513387            1       True          3
10    ExtraTreesMSE_BAG_L1  -38.107278       0.553487    8.316738                0.553487           8.316738            1       True          7
11  RandomForestMSE_BAG_L1  -38.424984       0.565053   16.730583                0.565053          16.730583            1       True          5
12   KNeighborsDist_BAG_L1  -84.125061       0.052075    0.032257                0.052075           0.032257            1       True          2
13   KNeighborsUnif_BAG_L1 -101.546199       0.055868    0.055365                0.055868           0.055365            1       True          1
Number of models trained: 14
Types of models trained:
{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_CatBoost', 'WeightedEnsembleModel', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_RF'}
Bagging used: True  (with 8 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])             : 2 | ['season', 'weather']
('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
('int', [])                  : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']
*** End of fit() summary ***
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"
  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"')
Out[52]:
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
  'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
  'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,
  'KNeighborsDist_BAG_L1': -84.12506123181602,
  'LightGBMXT_BAG_L1': -34.453884062670745,
  'LightGBM_BAG_L1': -33.91733862651761,
  'RandomForestMSE_BAG_L1': -38.424983594881716,
  'CatBoost_BAG_L1': -34.05600453907308,
  'ExtraTreesMSE_BAG_L1': -38.10727767243523,
  'WeightedEnsemble_L2': -32.17854848587382,
  'LightGBMXT_BAG_L2': -31.153360362923692,
  'LightGBM_BAG_L2': -30.656909399225658,
  'RandomForestMSE_BAG_L2': -31.67804049001475,
  'CatBoost_BAG_L2': -30.47847179918766,
  'ExtraTreesMSE_BAG_L2': -31.488287938277107,
  'WeightedEnsemble_L3': -30.229370529662308},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'KNeighborsUnif_BAG_L1': ['KNeighborsUnif_BAG_L1'],
  'KNeighborsDist_BAG_L1': ['KNeighborsDist_BAG_L1'],
  'LightGBMXT_BAG_L1': ['LightGBMXT_BAG_L1'],
  'LightGBM_BAG_L1': ['LightGBM_BAG_L1'],
  'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],
  'CatBoost_BAG_L1': ['CatBoost_BAG_L1'],
  'ExtraTreesMSE_BAG_L1': ['ExtraTreesMSE_BAG_L1'],
  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],
  'LightGBMXT_BAG_L2': ['LightGBMXT_BAG_L2'],
  'LightGBM_BAG_L2': ['LightGBM_BAG_L2'],
  'RandomForestMSE_BAG_L2': ['RandomForestMSE_BAG_L2'],
  'CatBoost_BAG_L2': ['CatBoost_BAG_L2'],
  'ExtraTreesMSE_BAG_L2': ['ExtraTreesMSE_BAG_L2'],
  'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},
 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.05536460876464844,
  'KNeighborsDist_BAG_L1': 0.032257080078125,
  'LightGBMXT_BAG_L1': 73.51338744163513,
  'LightGBM_BAG_L1': 25.2456796169281,
  'RandomForestMSE_BAG_L1': 16.73058319091797,
  'CatBoost_BAG_L1': 242.99554920196533,
  'ExtraTreesMSE_BAG_L1': 8.316738367080688,
  'WeightedEnsemble_L2': 0.39399123191833496,
  'LightGBMXT_BAG_L2': 15.12924313545227,
  'LightGBM_BAG_L2': 10.258209228515625,
  'RandomForestMSE_BAG_L2': 40.13529586791992,
  'CatBoost_BAG_L2': 120.83152604103088,
  'ExtraTreesMSE_BAG_L2': 12.317944288253784,
  'WeightedEnsemble_L3': 0.3199427127838135},
 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.055867910385131836,
  'KNeighborsDist_BAG_L1': 0.05207467079162598,
  'LightGBMXT_BAG_L1': 11.95328140258789,
  'LightGBM_BAG_L1': 3.0494155883789062,
  'RandomForestMSE_BAG_L1': 0.5650532245635986,
  'CatBoost_BAG_L1': 0.10098505020141602,
  'ExtraTreesMSE_BAG_L1': 0.5534873008728027,
  'WeightedEnsemble_L2': 0.0007586479187011719,
  'LightGBMXT_BAG_L2': 0.7138962745666504,
  'LightGBM_BAG_L2': 0.26184725761413574,
  'RandomForestMSE_BAG_L2': 0.6114168167114258,
  'CatBoost_BAG_L2': 0.057158708572387695,
  'ExtraTreesMSE_BAG_L2': 0.7529025077819824,
  'WeightedEnsemble_L3': 0.0006318092346191406},
 'num_bag_folds': 8,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBMXT_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'CatBoost_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBMXT_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'CatBoost_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'ExtraTreesMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                      model   score_val  pred_time_val    fit_time  \
 0      WeightedEnsemble_L3  -30.229371      17.975116  553.563776   
 1          CatBoost_BAG_L2  -30.478472      16.387324  487.721086   
 2          LightGBM_BAG_L2  -30.656909      16.592012  377.147769   
 3        LightGBMXT_BAG_L2  -31.153360      17.044061  382.018803   
 4     ExtraTreesMSE_BAG_L2  -31.488288      17.083068  379.207504   
 5   RandomForestMSE_BAG_L2  -31.678040      16.941582  407.024855   
 6      WeightedEnsemble_L2  -32.178548      15.721569  358.911448   
 7          LightGBM_BAG_L1  -33.917339       3.049416   25.245680   
 8          CatBoost_BAG_L1  -34.056005       0.100985  242.995549   
 9        LightGBMXT_BAG_L1  -34.453884      11.953281   73.513387   
 10    ExtraTreesMSE_BAG_L1  -38.107278       0.553487    8.316738   
 11  RandomForestMSE_BAG_L1  -38.424984       0.565053   16.730583   
 12   KNeighborsDist_BAG_L1  -84.125061       0.052075    0.032257   
 13   KNeighborsUnif_BAG_L1 -101.546199       0.055868    0.055365   
 
     pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                 0.000632           0.319943            3       True   
 1                 0.057159         120.831526            2       True   
 2                 0.261847          10.258209            2       True   
 3                 0.713896          15.129243            2       True   
 4                 0.752903          12.317944            2       True   
 5                 0.611417          40.135296            2       True   
 6                 0.000759           0.393991            2       True   
 7                 3.049416          25.245680            1       True   
 8                 0.100985         242.995549            1       True   
 9                11.953281          73.513387            1       True   
 10                0.553487           8.316738            1       True   
 11                0.565053          16.730583            1       True   
 12                0.052075           0.032257            1       True   
 13                0.055868           0.055365            1       True   
 
     fit_order  
 0          14  
 1          12  
 2          10  
 3           9  
 4          13  
 5          11  
 6           8  
 7           4  
 8           6  
 9           3  
 10          7  
 11          5  
 12          2  
 13          1  }
In [53]:
predictions_new_features = predictor_new_features.predict(test)
predictions_new_features = {'datetime': test['datetime'], 'Pred_count': predictions_new_features}
predictions_new_features = pd.DataFrame(data=predictions_new_features)
predictions_new_features.head()
Out[53]:
datetime Pred_count
0 2011-01-20 00:00:00 16.358873
1 2011-01-20 01:00:00 11.384792
2 2011-01-20 02:00:00 10.392972
3 2011-01-20 03:00:00 9.292883
4 2011-01-20 04:00:00 7.534829
In [54]:
predictions_new_features[predictions_new_features['Pred_count']<0] = 0
In [55]:
predictions_new_features.describe()
Out[55]:
datetime Pred_count
count 6493 6493.000000
mean 2012-01-13 09:27:47.765285632 153.902969
min 2011-01-20 00:00:00 1.313361
25% 2011-07-22 15:00:00 54.035172
50% 2012-01-20 23:00:00 119.526962
75% 2012-07-20 17:00:00 218.603973
max 2012-12-31 23:00:00 820.556641
std NaN 132.769485
In [56]:
# Same submitting predictions
submission_new_features = pd.read_csv('submission.csv')
submission_new_features["count"] = predictions_new_features['Pred_count']
submission_new_features.to_csv("submission_new_features.csv", index=False)
In [44]:
!kaggle competitions submit -c bike-sharing-demand -f submission_new_features.csv -m "new features"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 576kB/s]
Successfully submitted to Bike Sharing Demand
In [45]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName                     date                 description           status    publicScore  privateScore  
---------------------------  -------------------  --------------------  --------  -----------  ------------  
submission_new_features.csv  2024-04-30 02:37:04  new features          pending                              
submission.csv               2024-04-30 02:20:52  first raw submission  complete  1.80512      1.80512       
submission_new_features.csv  2024-04-28 22:14:56  new features          complete  0.65798      0.65798       
submission.csv               2024-04-28 22:02:47  first raw submission  complete  1.84007      1.84007       
In [ ]:
 
In [57]:
import autogluon.core as ag

nn_options = {  # specifies non-default hyperparameter values for neural network models
    'num_epochs': 10,  # number of training epochs (controls training time of NN models)
    'learning_rate': ag.space.Real(1e-4, 1e-2, default=5e-4, log=True),  # learning rate used in training (real-valued hyperparameter searched on log-scale)
    'activation': ag.space.Categorical('relu', 'softrelu', 'tanh'),  # activation function used in NN (categorical hyperparameter, default = first entry)
    'layers': ag.space.Categorical([100], [1000], [200, 100], [300, 200, 100]),  # each choice for categorical hyperparameter 'layers' corresponds to list of sizes for each NN layer to use
    'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1),  # dropout probability (real-valued hyperparameter)
}

gbm_options = {  # specifies non-default hyperparameter values for lightGBM gradient boosted trees
    'num_boost_round': 100,  # number of boosting rounds (controls training time of GBM models)
    'num_leaves': ag.space.Int(lower=26, upper=66, default=36),  # number of leaves in trees (integer hyperparameter)
}

hyperparameters = {  # hyperparameters of each model type
                   'GBM': gbm_options,
                   #'NN': nn_options,  # NOTE: comment this line out if you get errors on Mac OSX
                  }  # When these keys are missing from hyperparameters dict, no models of that type are trained

#num_trials = 5  # try at most 5 different hyperparameter configurations for each type of model
search_strategy = 'auto'  # to tune hyperparameters using Bayesian optimization routine with a local scheduler
hyperparameter_tune_kwargs = {  # HPO is not performed unless hyperparameter_tune_kwargs is specified
    #'num_trials': num_trials,
    'scheduler' : 'local',
    'searcher': search_strategy,
}

predictor_new_hpo = TabularPredictor(label="count", eval_metric="root_mean_squared_error",learner_kwargs={"ignored_columns":
["casual", "registered"]}).fit(train_data=train, time_limit=600, presets="best_quality", hyperparameters=hyperparameters, hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,
)
No path specified. Models will be saved in: "AutogluonModels/ag-20240430_035402"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/utils.py:564: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context("mode.use_inf_as_na", True):  # treat None, NaN, INF, NINF as NA
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20240430_035402"
AutoGluon Version:  0.8.2
Python Version:     3.10.14
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #1 SMP Sat Mar 23 09:49:55 UTC 2024
Disk Space Avail:   2.47 GB / 5.36 GB (46.1%)
	WARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception. 
	We recommend a minimum available disk space of 10 GB, and large datasets may require more.
Train Data Rows:    10886
Train Data Columns: 15
Label Column: count
Preprocessing data ...
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/utils.py:564: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context("mode.use_inf_as_na", True):  # treat None, NaN, INF, NINF as NA
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
	Label info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
/opt/conda/lib/python3.10/site-packages/autogluon/tabular/learner/default_learner.py:215: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context("mode.use_inf_as_na", True):  # treat None, NaN, INF, NINF as NA
Using Feature Generators to preprocess the data ...
Dropping user-specified ignored columns: ['casual', 'registered']
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    2249.49 MB
	Train Data (Original)  Memory Usage: 0.81 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Stage 5 Generators:
		Fitting DropDuplicatesFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) : 2 | ['season', 'weather']
		('datetime', []) : 1 | ['datetime']
		('float', [])    : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])      : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             : 2 | ['season', 'weather']
		('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                  : 4 | ['humidity', 'month', 'day', 'hour']
		('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']
		('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']
	0.7s = Fit runtime
	13 features in original data used to generate 15 features in processed data.
	Train Data (Processed) Memory Usage: 0.8 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.81s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
	To change this, specify the eval_metric parameter of Predictor()
User-specified model hyperparameters to be fit:
{
	'GBM': {'num_boost_round': 100, 'num_leaves': Int: lower=26, upper=66},
}
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 1 L1 models ...
Hyperparameter tuning model: LightGBM_BAG_L1 ... Tuning model for up to 359.43s of the 599.19s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
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	Ran out of time, early stopping on iteration 59. Best iteration is:
	[59]	valid_set's rmse: 143.534
	Ran out of time, early stopping on iteration 60. Best iteration is:
	[60]	valid_set's rmse: 147.596
	Ran out of time, early stopping on iteration 86. Best iteration is:
	[86]	valid_set's rmse: 133.845
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Ran out of time, early stopping on iteration 1. Best iteration is:
	[1]	valid_set's rmse: 167.609
	Stopping HPO to satisfy time limit...
Fitted model: LightGBM_BAG_L1/T1 ...
	-40.2554	 = Validation score   (-root_mean_squared_error)
	4.24s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T2 ...
	-39.2133	 = Validation score   (-root_mean_squared_error)
	4.24s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T3 ...
	-38.2356	 = Validation score   (-root_mean_squared_error)
	5.09s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T4 ...
	-122.1122	 = Validation score   (-root_mean_squared_error)
	4.24s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T5 ...
	-43.2684	 = Validation score   (-root_mean_squared_error)
	4.31s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T6 ...
	-109.5652	 = Validation score   (-root_mean_squared_error)
	4.44s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T7 ...
	-38.9229	 = Validation score   (-root_mean_squared_error)
	4.38s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T8 ...
	-36.344	 = Validation score   (-root_mean_squared_error)
	4.11s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T9 ...
	-107.8719	 = Validation score   (-root_mean_squared_error)
	3.8s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T10 ...
	-36.2276	 = Validation score   (-root_mean_squared_error)
	3.98s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T11 ...
	-75.4893	 = Validation score   (-root_mean_squared_error)
	4.9s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T12 ...
	-125.1145	 = Validation score   (-root_mean_squared_error)
	4.23s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T13 ...
	-41.4674	 = Validation score   (-root_mean_squared_error)
	4.25s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T14 ...
	-66.1791	 = Validation score   (-root_mean_squared_error)
	4.22s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T15 ...
	-110.3224	 = Validation score   (-root_mean_squared_error)
	4.53s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T16 ...
	-92.7813	 = Validation score   (-root_mean_squared_error)
	4.34s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T17 ...
	-61.6928	 = Validation score   (-root_mean_squared_error)
	4.49s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T18 ...
	-37.2716	 = Validation score   (-root_mean_squared_error)
	4.2s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T19 ...
	-40.9873	 = Validation score   (-root_mean_squared_error)
	4.61s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T20 ...
	-39.7361	 = Validation score   (-root_mean_squared_error)
	4.65s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T21 ...
	-63.5598	 = Validation score   (-root_mean_squared_error)
	4.6s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T22 ...
	-35.4298	 = Validation score   (-root_mean_squared_error)
	4.75s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T23 ...
	-35.9882	 = Validation score   (-root_mean_squared_error)
	4.62s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T24 ...
	-54.9086	 = Validation score   (-root_mean_squared_error)
	4.72s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T25 ...
	-111.0101	 = Validation score   (-root_mean_squared_error)
	4.44s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T26 ...
	-37.176	 = Validation score   (-root_mean_squared_error)
	4.41s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T27 ...
	-40.0321	 = Validation score   (-root_mean_squared_error)
	5.29s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T28 ...
	-36.6122	 = Validation score   (-root_mean_squared_error)
	4.24s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T29 ...
	-125.9376	 = Validation score   (-root_mean_squared_error)
	4.55s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T30 ...
	-67.2486	 = Validation score   (-root_mean_squared_error)
	4.99s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T31 ...
	-104.1926	 = Validation score   (-root_mean_squared_error)
	4.36s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T32 ...
	-67.7047	 = Validation score   (-root_mean_squared_error)
	4.83s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T33 ...
	-60.0798	 = Validation score   (-root_mean_squared_error)
	4.52s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T34 ...
	-35.1498	 = Validation score   (-root_mean_squared_error)
	4.53s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T35 ...
	-37.9905	 = Validation score   (-root_mean_squared_error)
	4.34s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T36 ...
	-37.3711	 = Validation score   (-root_mean_squared_error)
	4.7s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T37 ...
	-35.317	 = Validation score   (-root_mean_squared_error)
	4.29s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T38 ...
	-55.3058	 = Validation score   (-root_mean_squared_error)
	4.17s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T39 ...
	-38.5032	 = Validation score   (-root_mean_squared_error)
	4.6s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T40 ...
	-52.1212	 = Validation score   (-root_mean_squared_error)
	4.94s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T41 ...
	-45.2744	 = Validation score   (-root_mean_squared_error)
	3.93s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T42 ...
	-40.6689	 = Validation score   (-root_mean_squared_error)
	4.19s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T43 ...
	-100.128	 = Validation score   (-root_mean_squared_error)
	4.93s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T44 ...
	-74.8522	 = Validation score   (-root_mean_squared_error)
	4.07s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T45 ...
	-110.1831	 = Validation score   (-root_mean_squared_error)
	4.24s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T46 ...
	-35.2167	 = Validation score   (-root_mean_squared_error)
	4.58s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T47 ...
	-39.7212	 = Validation score   (-root_mean_squared_error)
	4.2s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T48 ...
	-103.0587	 = Validation score   (-root_mean_squared_error)
	3.74s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T49 ...
	-56.1877	 = Validation score   (-root_mean_squared_error)
	4.41s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T50 ...
	-66.9086	 = Validation score   (-root_mean_squared_error)
	4.39s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T51 ...
	-35.7845	 = Validation score   (-root_mean_squared_error)
	4.62s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T52 ...
	-47.2921	 = Validation score   (-root_mean_squared_error)
	3.96s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T53 ...
	-122.6625	 = Validation score   (-root_mean_squared_error)
	4.68s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T54 ...
	-68.1767	 = Validation score   (-root_mean_squared_error)
	4.43s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T55 ...
	-36.2983	 = Validation score   (-root_mean_squared_error)
	3.92s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T56 ...
	-87.7382	 = Validation score   (-root_mean_squared_error)
	4.29s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T57 ...
	-41.4776	 = Validation score   (-root_mean_squared_error)
	4.47s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T58 ...
	-67.0334	 = Validation score   (-root_mean_squared_error)
	4.32s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T59 ...
	-99.1388	 = Validation score   (-root_mean_squared_error)
	4.05s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T60 ...
	-39.0003	 = Validation score   (-root_mean_squared_error)
	4.53s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T61 ...
	-48.9962	 = Validation score   (-root_mean_squared_error)
	4.29s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T62 ...
	-38.7789	 = Validation score   (-root_mean_squared_error)
	4.95s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T63 ...
	-36.6609	 = Validation score   (-root_mean_squared_error)
	4.35s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T64 ...
	-42.7232	 = Validation score   (-root_mean_squared_error)
	3.53s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T65 ...
	-35.4953	 = Validation score   (-root_mean_squared_error)
	4.79s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T66 ...
	-36.7935	 = Validation score   (-root_mean_squared_error)
	4.95s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T67 ...
	-64.1554	 = Validation score   (-root_mean_squared_error)
	4.86s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T68 ...
	-75.0539	 = Validation score   (-root_mean_squared_error)
	5.11s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T69 ...
	-40.9699	 = Validation score   (-root_mean_squared_error)
	4.14s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T70 ...
	-36.9626	 = Validation score   (-root_mean_squared_error)
	4.54s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T71 ...
	-87.995	 = Validation score   (-root_mean_squared_error)
	4.4s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T72 ...
	-94.3637	 = Validation score   (-root_mean_squared_error)
	4.4s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T73 ...
	-86.6167	 = Validation score   (-root_mean_squared_error)
	3.84s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T74 ...
	-104.3449	 = Validation score   (-root_mean_squared_error)
	3.92s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T75 ...
	-36.9081	 = Validation score   (-root_mean_squared_error)
	4.36s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T76 ...
	-90.7502	 = Validation score   (-root_mean_squared_error)
	4.45s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T77 ...
	-61.2038	 = Validation score   (-root_mean_squared_error)
	4.38s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T78 ...
	-86.3004	 = Validation score   (-root_mean_squared_error)
	4.45s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T79 ...
	-117.0847	 = Validation score   (-root_mean_squared_error)
	4.39s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T80 ...
	-129.4395	 = Validation score   (-root_mean_squared_error)
	4.48s	 = Training   runtime
	0.0s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 359.99s of the 239.46s of remaining time.
	-34.3142	 = Validation score   (-root_mean_squared_error)
	0.54s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 1 L2 models ...
Hyperparameter tuning model: LightGBM_BAG_L2 ... Tuning model for up to 214.99s of the 238.82s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
	Stopping HPO to satisfy time limit...
Fitted model: LightGBM_BAG_L2/T1 ...
	-34.1403	 = Validation score   (-root_mean_squared_error)
	16.41s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T2 ...
	-34.1614	 = Validation score   (-root_mean_squared_error)
	12.85s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T3 ...
	-34.2884	 = Validation score   (-root_mean_squared_error)
	20.69s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T4 ...
	-101.9903	 = Validation score   (-root_mean_squared_error)
	13.7s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T5 ...
	-34.5473	 = Validation score   (-root_mean_squared_error)
	17.21s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T6 ...
	-98.9079	 = Validation score   (-root_mean_squared_error)
	19.6s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T7 ...
	-34.0964	 = Validation score   (-root_mean_squared_error)
	11.57s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T8 ...
	-34.245	 = Validation score   (-root_mean_squared_error)
	18.52s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T9 ...
	-88.9405	 = Validation score   (-root_mean_squared_error)
	14.15s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T10 ...
	-34.192	 = Validation score   (-root_mean_squared_error)
	12.91s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T11 ...
	-58.0167	 = Validation score   (-root_mean_squared_error)
	15.96s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T12 ...
	-109.5844	 = Validation score   (-root_mean_squared_error)
	15.36s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T13 ...
	-34.1249	 = Validation score   (-root_mean_squared_error)
	14.19s	 = Training   runtime
	0.0s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 35.33s of remaining time.
	-33.8989	 = Validation score   (-root_mean_squared_error)
	0.27s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 564.96s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20240430_035402")
In [58]:
predictor_new_hpo.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
                  model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0   WeightedEnsemble_L3  -33.898886       0.010503  407.617138                0.000579           0.265508            3       True         95
1    LightGBM_BAG_L2/T7  -34.096427       0.009594  365.179346                0.000089          11.569162            2       True         88
2   LightGBM_BAG_L2/T13  -34.124900       0.009602  367.799219                0.000097          14.189035            2       True         94
3    LightGBM_BAG_L2/T1  -34.140293       0.009596  370.022974                0.000090          16.412791            2       True         82
4    LightGBM_BAG_L2/T2  -34.161413       0.009652  366.456404                0.000146          12.846220            2       True         83
5   LightGBM_BAG_L2/T10  -34.192016       0.009598  366.523458                0.000093          12.913274            2       True         91
6    LightGBM_BAG_L2/T8  -34.245007       0.009710  372.134456                0.000205          18.524272            2       True         89
7    LightGBM_BAG_L2/T3  -34.288362       0.009656  374.302429                0.000150          20.692245            2       True         84
8   WeightedEnsemble_L2  -34.314234       0.001973   23.483544                0.001294           0.539096            2       True         81
9    LightGBM_BAG_L2/T5  -34.547295       0.009705  370.820895                0.000199          17.210711            2       True         86
10  LightGBM_BAG_L1/T34  -35.149822       0.000118    4.527263                0.000118           4.527263            1       True         34
11  LightGBM_BAG_L1/T46  -35.216673       0.000098    4.581206                0.000098           4.581206            1       True         46
12  LightGBM_BAG_L1/T37  -35.317043       0.000246    4.293521                0.000246           4.293521            1       True         37
13  LightGBM_BAG_L1/T22  -35.429809       0.000102    4.748456                0.000102           4.748456            1       True         22
14  LightGBM_BAG_L1/T65  -35.495328       0.000115    4.794003                0.000115           4.794003            1       True         65
15  LightGBM_BAG_L1/T51  -35.784493       0.000094    4.622752                0.000094           4.622752            1       True         51
16  LightGBM_BAG_L1/T23  -35.988230       0.000094    4.619236                0.000094           4.619236            1       True         23
17  LightGBM_BAG_L1/T10  -36.227587       0.000094    3.982505                0.000094           3.982505            1       True         10
18  LightGBM_BAG_L1/T55  -36.298321       0.000102    3.918072                0.000102           3.918072            1       True         55
19   LightGBM_BAG_L1/T8  -36.343987       0.000085    4.109572                0.000085           4.109572            1       True          8
20  LightGBM_BAG_L1/T28  -36.612212       0.000119    4.235139                0.000119           4.235139            1       True         28
21  LightGBM_BAG_L1/T63  -36.660925       0.000093    4.346629                0.000093           4.346629            1       True         63
22  LightGBM_BAG_L1/T66  -36.793458       0.000106    4.949643                0.000106           4.949643            1       True         66
23  LightGBM_BAG_L1/T75  -36.908097       0.000104    4.355204                0.000104           4.355204            1       True         75
24  LightGBM_BAG_L1/T70  -36.962596       0.000119    4.537135                0.000119           4.537135            1       True         70
25  LightGBM_BAG_L1/T26  -37.176050       0.000091    4.412049                0.000091           4.412049            1       True         26
26  LightGBM_BAG_L1/T18  -37.271566       0.000111    4.195667                0.000111           4.195667            1       True         18
27  LightGBM_BAG_L1/T36  -37.371118       0.000097    4.698167                0.000097           4.698167            1       True         36
28  LightGBM_BAG_L1/T35  -37.990452       0.000098    4.342577                0.000098           4.342577            1       True         35
29   LightGBM_BAG_L1/T3  -38.235570       0.000106    5.088213                0.000106           5.088213            1       True          3
30  LightGBM_BAG_L1/T39  -38.503157       0.000106    4.596222                0.000106           4.596222            1       True         39
31  LightGBM_BAG_L1/T62  -38.778897       0.000251    4.949531                0.000251           4.949531            1       True         62
32   LightGBM_BAG_L1/T7  -38.922926       0.000089    4.378773                0.000089           4.378773            1       True          7
33  LightGBM_BAG_L1/T60  -39.000336       0.000098    4.526189                0.000098           4.526189            1       True         60
34   LightGBM_BAG_L1/T2  -39.213259       0.000087    4.244906                0.000087           4.244906            1       True          2
35  LightGBM_BAG_L1/T47  -39.721217       0.000091    4.198434                0.000091           4.198434            1       True         47
36  LightGBM_BAG_L1/T20  -39.736145       0.000105    4.650360                0.000105           4.650360            1       True         20
37  LightGBM_BAG_L1/T27  -40.032105       0.000103    5.292323                0.000103           5.292323            1       True         27
38   LightGBM_BAG_L1/T1  -40.255449       0.000103    4.240043                0.000103           4.240043            1       True          1
39  LightGBM_BAG_L1/T42  -40.668913       0.000108    4.188065                0.000108           4.188065            1       True         42
40  LightGBM_BAG_L1/T69  -40.969918       0.000094    4.144487                0.000094           4.144487            1       True         69
41  LightGBM_BAG_L1/T19  -40.987305       0.000106    4.605637                0.000106           4.605637            1       True         19
42  LightGBM_BAG_L1/T13  -41.467370       0.000091    4.251490                0.000091           4.251490            1       True         13
43  LightGBM_BAG_L1/T57  -41.477611       0.000097    4.465062                0.000097           4.465062            1       True         57
44  LightGBM_BAG_L1/T64  -42.723153       0.000102    3.525716                0.000102           3.525716            1       True         64
45   LightGBM_BAG_L1/T5  -43.268413       0.000089    4.307853                0.000089           4.307853            1       True          5
46  LightGBM_BAG_L1/T41  -45.274366       0.000089    3.932689                0.000089           3.932689            1       True         41
47  LightGBM_BAG_L1/T52  -47.292121       0.000120    3.955607                0.000120           3.955607            1       True         52
48  LightGBM_BAG_L1/T61  -48.996161       0.000280    4.292532                0.000280           4.292532            1       True         61
49  LightGBM_BAG_L1/T40  -52.121229       0.000098    4.940718                0.000098           4.940718            1       True         40
50  LightGBM_BAG_L1/T24  -54.908565       0.000091    4.718335                0.000091           4.718335            1       True         24
51  LightGBM_BAG_L1/T38  -55.305840       0.000094    4.170742                0.000094           4.170742            1       True         38
52  LightGBM_BAG_L1/T49  -56.187750       0.000097    4.410849                0.000097           4.410849            1       True         49
53  LightGBM_BAG_L2/T11  -58.016681       0.009601  369.568624                0.000095          15.958440            2       True         92
54  LightGBM_BAG_L1/T33  -60.079767       0.000090    4.523161                0.000090           4.523161            1       True         33
55  LightGBM_BAG_L1/T77  -61.203781       0.000104    4.376895                0.000104           4.376895            1       True         77
56  LightGBM_BAG_L1/T17  -61.692829       0.000343    4.489034                0.000343           4.489034            1       True         17
57  LightGBM_BAG_L1/T21  -63.559775       0.000092    4.603836                0.000092           4.603836            1       True         21
58  LightGBM_BAG_L1/T67  -64.155367       0.000186    4.858462                0.000186           4.858462            1       True         67
59  LightGBM_BAG_L1/T14  -66.179133       0.000126    4.223377                0.000126           4.223377            1       True         14
60  LightGBM_BAG_L1/T50  -66.908631       0.000101    4.391414                0.000101           4.391414            1       True         50
61  LightGBM_BAG_L1/T58  -67.033404       0.000124    4.319664                0.000124           4.319664            1       True         58
62  LightGBM_BAG_L1/T30  -67.248640       0.000136    4.989897                0.000136           4.989897            1       True         30
63  LightGBM_BAG_L1/T32  -67.704651       0.000353    4.826960                0.000353           4.826960            1       True         32
64  LightGBM_BAG_L1/T54  -68.176691       0.000205    4.430502                0.000205           4.430502            1       True         54
65  LightGBM_BAG_L1/T44  -74.852164       0.000098    4.073867                0.000098           4.073867            1       True         44
66  LightGBM_BAG_L1/T68  -75.053871       0.000134    5.106324                0.000134           5.106324            1       True         68
67  LightGBM_BAG_L1/T11  -75.489342       0.000092    4.896541                0.000092           4.896541            1       True         11
68  LightGBM_BAG_L1/T78  -86.300428       0.000087    4.446325                0.000087           4.446325            1       True         78
69  LightGBM_BAG_L1/T73  -86.616737       0.000096    3.838663                0.000096           3.838663            1       True         73
70  LightGBM_BAG_L1/T56  -87.738230       0.000088    4.285486                0.000088           4.285486            1       True         56
71  LightGBM_BAG_L1/T71  -87.995021       0.000102    4.396511                0.000102           4.396511            1       True         71
72   LightGBM_BAG_L2/T9  -88.940490       0.009602  367.759040                0.000096          14.148857            2       True         90
73  LightGBM_BAG_L1/T76  -90.750232       0.000107    4.445532                0.000107           4.445532            1       True         76
74  LightGBM_BAG_L1/T16  -92.781290       0.000100    4.344057                0.000100           4.344057            1       True         16
75  LightGBM_BAG_L1/T72  -94.363749       0.000122    4.395237                0.000122           4.395237            1       True         72
76   LightGBM_BAG_L2/T6  -98.907938       0.009645  373.213178                0.000140          19.602994            2       True         87
77  LightGBM_BAG_L1/T59  -99.138833       0.000087    4.045690                0.000087           4.045690            1       True         59
78  LightGBM_BAG_L1/T43 -100.128027       0.000109    4.929899                0.000109           4.929899            1       True         43
79   LightGBM_BAG_L2/T4 -101.990278       0.010666  367.313714                0.001160          13.703530            2       True         85
80  LightGBM_BAG_L1/T48 -103.058724       0.000088    3.743199                0.000088           3.743199            1       True         48
81  LightGBM_BAG_L1/T31 -104.192597       0.000112    4.362969                0.000112           4.362969            1       True         31
82  LightGBM_BAG_L1/T74 -104.344855       0.000115    3.915456                0.000115           3.915456            1       True         74
83   LightGBM_BAG_L1/T9 -107.871924       0.000101    3.799279                0.000101           3.799279            1       True          9
84   LightGBM_BAG_L1/T6 -109.565161       0.000120    4.436033                0.000120           4.436033            1       True          6
85  LightGBM_BAG_L2/T12 -109.584386       0.009593  368.969010                0.000087          15.358827            2       True         93
86  LightGBM_BAG_L1/T45 -110.183062       0.000175    4.238026                0.000175           4.238026            1       True         45
87  LightGBM_BAG_L1/T15 -110.322411       0.000110    4.532952                0.000110           4.532952            1       True         15
88  LightGBM_BAG_L1/T25 -111.010055       0.000089    4.442690                0.000089           4.442690            1       True         25
89  LightGBM_BAG_L1/T79 -117.084696       0.000098    4.388314                0.000098           4.388314            1       True         79
90   LightGBM_BAG_L1/T4 -122.112198       0.000085    4.237734                0.000085           4.237734            1       True          4
91  LightGBM_BAG_L1/T53 -122.662496       0.000104    4.676295                0.000104           4.676295            1       True         53
92  LightGBM_BAG_L1/T12 -125.114496       0.000091    4.229833                0.000091           4.229833            1       True         12
93  LightGBM_BAG_L1/T29 -125.937609       0.000221    4.547218                0.000221           4.547218            1       True         29
94  LightGBM_BAG_L1/T80 -129.439519       0.000129    4.479278                0.000129           4.479278            1       True         80
Number of models trained: 95
Types of models trained:
{'StackerEnsembleModel_LGB', 'WeightedEnsembleModel'}
Bagging used: True  (with 8 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])             : 2 | ['season', 'weather']
('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
('int', [])                  : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']
*** End of fit() summary ***
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"
  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"')
Out[58]:
{'model_types': {'LightGBM_BAG_L1/T1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T3': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T4': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T5': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T6': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T7': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T8': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T9': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T10': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T11': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T12': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T13': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T14': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T15': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T16': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T17': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T18': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T19': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T20': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T21': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T22': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T23': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T24': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T25': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T26': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T27': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T28': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T29': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T30': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T31': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T32': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T33': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T34': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T35': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T36': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T37': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T38': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T39': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T40': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T41': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T42': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T43': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T44': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T45': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T46': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T47': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T48': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T49': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T50': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T51': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T52': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T53': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T54': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T55': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T56': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T57': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T58': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T59': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T60': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T61': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T62': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T63': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T64': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T65': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T66': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T67': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T68': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T69': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T70': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T71': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T72': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T73': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T74': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T75': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T76': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T77': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T78': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T79': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T80': 'StackerEnsembleModel_LGB',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBM_BAG_L2/T1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T3': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T4': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T5': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T6': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T7': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T8': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T9': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T10': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T11': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T12': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T13': 'StackerEnsembleModel_LGB',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'LightGBM_BAG_L1/T1': -40.255448619289915,
  'LightGBM_BAG_L1/T2': -39.213258999645646,
  'LightGBM_BAG_L1/T3': -38.23556976473342,
  'LightGBM_BAG_L1/T4': -122.11219756067042,
  'LightGBM_BAG_L1/T5': -43.26841303192551,
  'LightGBM_BAG_L1/T6': -109.56516071183998,
  'LightGBM_BAG_L1/T7': -38.92292646653962,
  'LightGBM_BAG_L1/T8': -36.34398653040369,
  'LightGBM_BAG_L1/T9': -107.87192403470239,
  'LightGBM_BAG_L1/T10': -36.227586894672875,
  'LightGBM_BAG_L1/T11': -75.48934220797054,
  'LightGBM_BAG_L1/T12': -125.11449594845834,
  'LightGBM_BAG_L1/T13': -41.46737021004442,
  'LightGBM_BAG_L1/T14': -66.17913287657221,
  'LightGBM_BAG_L1/T15': -110.32241088996467,
  'LightGBM_BAG_L1/T16': -92.78128995015007,
  'LightGBM_BAG_L1/T17': -61.69282927558979,
  'LightGBM_BAG_L1/T18': -37.27156582907592,
  'LightGBM_BAG_L1/T19': -40.98730528087555,
  'LightGBM_BAG_L1/T20': -39.73614543593702,
  'LightGBM_BAG_L1/T21': -63.559775057766394,
  'LightGBM_BAG_L1/T22': -35.42980889901937,
  'LightGBM_BAG_L1/T23': -35.9882297921665,
  'LightGBM_BAG_L1/T24': -54.90856464641669,
  'LightGBM_BAG_L1/T25': -111.01005508906172,
  'LightGBM_BAG_L1/T26': -37.176049689052626,
  'LightGBM_BAG_L1/T27': -40.032104882894814,
  'LightGBM_BAG_L1/T28': -36.61221217042727,
  'LightGBM_BAG_L1/T29': -125.937608816907,
  'LightGBM_BAG_L1/T30': -67.2486395772891,
  'LightGBM_BAG_L1/T31': -104.19259731826092,
  'LightGBM_BAG_L1/T32': -67.70465133015458,
  'LightGBM_BAG_L1/T33': -60.079767357924105,
  'LightGBM_BAG_L1/T34': -35.149822410680365,
  'LightGBM_BAG_L1/T35': -37.990451931898036,
  'LightGBM_BAG_L1/T36': -37.37111766822345,
  'LightGBM_BAG_L1/T37': -35.317042834910715,
  'LightGBM_BAG_L1/T38': -55.30583968339698,
  'LightGBM_BAG_L1/T39': -38.50315702968034,
  'LightGBM_BAG_L1/T40': -52.12122913225924,
  'LightGBM_BAG_L1/T41': -45.27436556632836,
  'LightGBM_BAG_L1/T42': -40.668913497247416,
  'LightGBM_BAG_L1/T43': -100.12802686038609,
  'LightGBM_BAG_L1/T44': -74.85216417136044,
  'LightGBM_BAG_L1/T45': -110.1830618948282,
  'LightGBM_BAG_L1/T46': -35.21667306620509,
  'LightGBM_BAG_L1/T47': -39.721217397974165,
  'LightGBM_BAG_L1/T48': -103.05872423774156,
  'LightGBM_BAG_L1/T49': -56.18774957897235,
  'LightGBM_BAG_L1/T50': -66.90863120156216,
  'LightGBM_BAG_L1/T51': -35.784492646992994,
  'LightGBM_BAG_L1/T52': -47.29212086814797,
  'LightGBM_BAG_L1/T53': -122.66249551011755,
  'LightGBM_BAG_L1/T54': -68.1766905794483,
  'LightGBM_BAG_L1/T55': -36.29832054659419,
  'LightGBM_BAG_L1/T56': -87.73822993514536,
  'LightGBM_BAG_L1/T57': -41.47761058864393,
  'LightGBM_BAG_L1/T58': -67.03340397848923,
  'LightGBM_BAG_L1/T59': -99.1388328111989,
  'LightGBM_BAG_L1/T60': -39.00033572332812,
  'LightGBM_BAG_L1/T61': -48.99616096267388,
  'LightGBM_BAG_L1/T62': -38.77889721076665,
  'LightGBM_BAG_L1/T63': -36.660925345082724,
  'LightGBM_BAG_L1/T64': -42.723153097325685,
  'LightGBM_BAG_L1/T65': -35.49532831933309,
  'LightGBM_BAG_L1/T66': -36.793457898655774,
  'LightGBM_BAG_L1/T67': -64.15536691123464,
  'LightGBM_BAG_L1/T68': -75.05387068377657,
  'LightGBM_BAG_L1/T69': -40.969917645368916,
  'LightGBM_BAG_L1/T70': -36.962595941642114,
  'LightGBM_BAG_L1/T71': -87.99502123312813,
  'LightGBM_BAG_L1/T72': -94.36374923901413,
  'LightGBM_BAG_L1/T73': -86.61673655644546,
  'LightGBM_BAG_L1/T74': -104.34485546217878,
  'LightGBM_BAG_L1/T75': -36.908097162124854,
  'LightGBM_BAG_L1/T76': -90.75023167669407,
  'LightGBM_BAG_L1/T77': -61.2037807850615,
  'LightGBM_BAG_L1/T78': -86.30042761751068,
  'LightGBM_BAG_L1/T79': -117.0846955084417,
  'LightGBM_BAG_L1/T80': -129.43951877809147,
  'WeightedEnsemble_L2': -34.31423439471698,
  'LightGBM_BAG_L2/T1': -34.14029252527811,
  'LightGBM_BAG_L2/T2': -34.16141314086803,
  'LightGBM_BAG_L2/T3': -34.28836161177525,
  'LightGBM_BAG_L2/T4': -101.99027789066632,
  'LightGBM_BAG_L2/T5': -34.54729509955411,
  'LightGBM_BAG_L2/T6': -98.90793750015261,
  'LightGBM_BAG_L2/T7': -34.09642659825696,
  'LightGBM_BAG_L2/T8': -34.24500728072462,
  'LightGBM_BAG_L2/T9': -88.94049048096228,
  'LightGBM_BAG_L2/T10': -34.19201565356319,
  'LightGBM_BAG_L2/T11': -58.01668051164485,
  'LightGBM_BAG_L2/T12': -109.58438636695975,
  'LightGBM_BAG_L2/T13': -34.12489987081852,
  'WeightedEnsemble_L3': -33.89888612553612},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'LightGBM_BAG_L1/T1': ['LightGBM_BAG_L1', 'T1'],
  'LightGBM_BAG_L1/T2': ['LightGBM_BAG_L1', 'T2'],
  'LightGBM_BAG_L1/T3': ['LightGBM_BAG_L1', 'T3'],
  'LightGBM_BAG_L1/T4': ['LightGBM_BAG_L1', 'T4'],
  'LightGBM_BAG_L1/T5': ['LightGBM_BAG_L1', 'T5'],
  'LightGBM_BAG_L1/T6': ['LightGBM_BAG_L1', 'T6'],
  'LightGBM_BAG_L1/T7': ['LightGBM_BAG_L1', 'T7'],
  'LightGBM_BAG_L1/T8': ['LightGBM_BAG_L1', 'T8'],
  'LightGBM_BAG_L1/T9': ['LightGBM_BAG_L1', 'T9'],
  'LightGBM_BAG_L1/T10': ['LightGBM_BAG_L1', 'T10'],
  'LightGBM_BAG_L1/T11': ['LightGBM_BAG_L1', 'T11'],
  'LightGBM_BAG_L1/T12': ['LightGBM_BAG_L1', 'T12'],
  'LightGBM_BAG_L1/T13': ['LightGBM_BAG_L1', 'T13'],
  'LightGBM_BAG_L1/T14': ['LightGBM_BAG_L1', 'T14'],
  'LightGBM_BAG_L1/T15': ['LightGBM_BAG_L1', 'T15'],
  'LightGBM_BAG_L1/T16': ['LightGBM_BAG_L1', 'T16'],
  'LightGBM_BAG_L1/T17': ['LightGBM_BAG_L1', 'T17'],
  'LightGBM_BAG_L1/T18': ['LightGBM_BAG_L1', 'T18'],
  'LightGBM_BAG_L1/T19': ['LightGBM_BAG_L1', 'T19'],
  'LightGBM_BAG_L1/T20': ['LightGBM_BAG_L1', 'T20'],
  'LightGBM_BAG_L1/T21': ['LightGBM_BAG_L1', 'T21'],
  'LightGBM_BAG_L1/T22': ['LightGBM_BAG_L1', 'T22'],
  'LightGBM_BAG_L1/T23': ['LightGBM_BAG_L1', 'T23'],
  'LightGBM_BAG_L1/T24': ['LightGBM_BAG_L1', 'T24'],
  'LightGBM_BAG_L1/T25': ['LightGBM_BAG_L1', 'T25'],
  'LightGBM_BAG_L1/T26': ['LightGBM_BAG_L1', 'T26'],
  'LightGBM_BAG_L1/T27': ['LightGBM_BAG_L1', 'T27'],
  'LightGBM_BAG_L1/T28': ['LightGBM_BAG_L1', 'T28'],
  'LightGBM_BAG_L1/T29': ['LightGBM_BAG_L1', 'T29'],
  'LightGBM_BAG_L1/T30': ['LightGBM_BAG_L1', 'T30'],
  'LightGBM_BAG_L1/T31': ['LightGBM_BAG_L1', 'T31'],
  'LightGBM_BAG_L1/T32': ['LightGBM_BAG_L1', 'T32'],
  'LightGBM_BAG_L1/T33': ['LightGBM_BAG_L1', 'T33'],
  'LightGBM_BAG_L1/T34': ['LightGBM_BAG_L1', 'T34'],
  'LightGBM_BAG_L1/T35': ['LightGBM_BAG_L1', 'T35'],
  'LightGBM_BAG_L1/T36': ['LightGBM_BAG_L1', 'T36'],
  'LightGBM_BAG_L1/T37': ['LightGBM_BAG_L1', 'T37'],
  'LightGBM_BAG_L1/T38': ['LightGBM_BAG_L1', 'T38'],
  'LightGBM_BAG_L1/T39': ['LightGBM_BAG_L1', 'T39'],
  'LightGBM_BAG_L1/T40': ['LightGBM_BAG_L1', 'T40'],
  'LightGBM_BAG_L1/T41': ['LightGBM_BAG_L1', 'T41'],
  'LightGBM_BAG_L1/T42': ['LightGBM_BAG_L1', 'T42'],
  'LightGBM_BAG_L1/T43': ['LightGBM_BAG_L1', 'T43'],
  'LightGBM_BAG_L1/T44': ['LightGBM_BAG_L1', 'T44'],
  'LightGBM_BAG_L1/T45': ['LightGBM_BAG_L1', 'T45'],
  'LightGBM_BAG_L1/T46': ['LightGBM_BAG_L1', 'T46'],
  'LightGBM_BAG_L1/T47': ['LightGBM_BAG_L1', 'T47'],
  'LightGBM_BAG_L1/T48': ['LightGBM_BAG_L1', 'T48'],
  'LightGBM_BAG_L1/T49': ['LightGBM_BAG_L1', 'T49'],
  'LightGBM_BAG_L1/T50': ['LightGBM_BAG_L1', 'T50'],
  'LightGBM_BAG_L1/T51': ['LightGBM_BAG_L1', 'T51'],
  'LightGBM_BAG_L1/T52': ['LightGBM_BAG_L1', 'T52'],
  'LightGBM_BAG_L1/T53': ['LightGBM_BAG_L1', 'T53'],
  'LightGBM_BAG_L1/T54': ['LightGBM_BAG_L1', 'T54'],
  'LightGBM_BAG_L1/T55': ['LightGBM_BAG_L1', 'T55'],
  'LightGBM_BAG_L1/T56': ['LightGBM_BAG_L1', 'T56'],
  'LightGBM_BAG_L1/T57': ['LightGBM_BAG_L1', 'T57'],
  'LightGBM_BAG_L1/T58': ['LightGBM_BAG_L1', 'T58'],
  'LightGBM_BAG_L1/T59': ['LightGBM_BAG_L1', 'T59'],
  'LightGBM_BAG_L1/T60': ['LightGBM_BAG_L1', 'T60'],
  'LightGBM_BAG_L1/T61': ['LightGBM_BAG_L1', 'T61'],
  'LightGBM_BAG_L1/T62': ['LightGBM_BAG_L1', 'T62'],
  'LightGBM_BAG_L1/T63': ['LightGBM_BAG_L1', 'T63'],
  'LightGBM_BAG_L1/T64': ['LightGBM_BAG_L1', 'T64'],
  'LightGBM_BAG_L1/T65': ['LightGBM_BAG_L1', 'T65'],
  'LightGBM_BAG_L1/T66': ['LightGBM_BAG_L1', 'T66'],
  'LightGBM_BAG_L1/T67': ['LightGBM_BAG_L1', 'T67'],
  'LightGBM_BAG_L1/T68': ['LightGBM_BAG_L1', 'T68'],
  'LightGBM_BAG_L1/T69': ['LightGBM_BAG_L1', 'T69'],
  'LightGBM_BAG_L1/T70': ['LightGBM_BAG_L1', 'T70'],
  'LightGBM_BAG_L1/T71': ['LightGBM_BAG_L1', 'T71'],
  'LightGBM_BAG_L1/T72': ['LightGBM_BAG_L1', 'T72'],
  'LightGBM_BAG_L1/T73': ['LightGBM_BAG_L1', 'T73'],
  'LightGBM_BAG_L1/T74': ['LightGBM_BAG_L1', 'T74'],
  'LightGBM_BAG_L1/T75': ['LightGBM_BAG_L1', 'T75'],
  'LightGBM_BAG_L1/T76': ['LightGBM_BAG_L1', 'T76'],
  'LightGBM_BAG_L1/T77': ['LightGBM_BAG_L1', 'T77'],
  'LightGBM_BAG_L1/T78': ['LightGBM_BAG_L1', 'T78'],
  'LightGBM_BAG_L1/T79': ['LightGBM_BAG_L1', 'T79'],
  'LightGBM_BAG_L1/T80': ['LightGBM_BAG_L1', 'T80'],
  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],
  'LightGBM_BAG_L2/T1': ['LightGBM_BAG_L2', 'T1'],
  'LightGBM_BAG_L2/T2': ['LightGBM_BAG_L2', 'T2'],
  'LightGBM_BAG_L2/T3': ['LightGBM_BAG_L2', 'T3'],
  'LightGBM_BAG_L2/T4': ['LightGBM_BAG_L2', 'T4'],
  'LightGBM_BAG_L2/T5': ['LightGBM_BAG_L2', 'T5'],
  'LightGBM_BAG_L2/T6': ['LightGBM_BAG_L2', 'T6'],
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  'LightGBM_BAG_L2/T8': ['LightGBM_BAG_L2', 'T8'],
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   'save_bag_folds': True},
  'LightGBM_BAG_L1/T40': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T41': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T42': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T43': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T44': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T45': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T46': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T47': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T48': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T49': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T50': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T51': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T52': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T53': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T54': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T55': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T56': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T57': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T58': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T59': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T60': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T61': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T62': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T63': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T64': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T65': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T66': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T67': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T68': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T69': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T70': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T71': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T72': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T73': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T74': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T75': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T76': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T77': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T78': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T79': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T80': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T3': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T4': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T5': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T6': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T7': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T8': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T9': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T10': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T11': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T12': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T13': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                   model   score_val  pred_time_val    fit_time  \
 0   WeightedEnsemble_L3  -33.898886       0.010503  407.617138   
 1    LightGBM_BAG_L2/T7  -34.096427       0.009594  365.179346   
 2   LightGBM_BAG_L2/T13  -34.124900       0.009602  367.799219   
 3    LightGBM_BAG_L2/T1  -34.140293       0.009596  370.022974   
 4    LightGBM_BAG_L2/T2  -34.161413       0.009652  366.456404   
 ..                  ...         ...            ...         ...   
 90   LightGBM_BAG_L1/T4 -122.112198       0.000085    4.237734   
 91  LightGBM_BAG_L1/T53 -122.662496       0.000104    4.676295   
 92  LightGBM_BAG_L1/T12 -125.114496       0.000091    4.229833   
 93  LightGBM_BAG_L1/T29 -125.937609       0.000221    4.547218   
 94  LightGBM_BAG_L1/T80 -129.439519       0.000129    4.479278   
 
     pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                 0.000579           0.265508            3       True   
 1                 0.000089          11.569162            2       True   
 2                 0.000097          14.189035            2       True   
 3                 0.000090          16.412791            2       True   
 4                 0.000146          12.846220            2       True   
 ..                     ...                ...          ...        ...   
 90                0.000085           4.237734            1       True   
 91                0.000104           4.676295            1       True   
 92                0.000091           4.229833            1       True   
 93                0.000221           4.547218            1       True   
 94                0.000129           4.479278            1       True   
 
     fit_order  
 0          95  
 1          88  
 2          94  
 3          82  
 4          83  
 ..        ...  
 90          4  
 91         53  
 92         12  
 93         29  
 94         80  
 
 [95 rows x 9 columns]}
In [59]:
new_predictions_hpo = predictor_new_hpo.predict(test)
new_predictions_hpo[new_predictions_hpo<0] = 0
In [60]:
# Same submitting predictions
submission_new_hpo = pd.read_csv("submission.csv", parse_dates=["datetime"])
submission_new_hpo["count"] = new_predictions_hpo
submission_new_hpo.to_csv("submission_new_hpo.csv", index=False)
In [61]:
!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo.csv -m "new features with hyperparameters"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 673kB/s]
Successfully submitted to Bike Sharing Demand
In [62]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName                     date                 description                        status    publicScore  privateScore  
---------------------------  -------------------  ---------------------------------  --------  -----------  ------------  
submission_new_hpo.csv       2024-04-30 04:04:03  new features with hyperparameters  complete  0.47887      0.47887       
submission.csv               2024-04-30 03:42:29  first raw submission               complete  1.80337      1.80337       
submission_new_hpo.csv       2024-04-30 02:53:22  new features with hyperparameters  complete  0.48188      0.48188       
submission_new_features.csv  2024-04-30 02:37:04  new features                       complete  0.6741       0.6741        
In [63]:
fig = pd.DataFrame(
    {
        "model": ["initial", "add_features", "hpo"],
        "score": [-114.766567, -35.146287, -41.271647]
    }
).plot(x="model", y="score", figsize=(10, 8)).get_figure()
fig.savefig('model_train_score.png')
No description has been provided for this image
In [64]:
# Take the 3 kaggle scores and creating a line plot to show improvement
fig = pd.DataFrame(
    {
        "test_eval": ["initial", "add_features", "hpo"],
        "score": [1.39373, 0.46870, 0.49696]
    }
).plot(x="test_eval", y="score", figsize=(10, 8)).get_figure()
fig.savefig('model_test_score.png')
No description has been provided for this image
In [65]:
pd.DataFrame({
    "model": ["initial", "add_features", "hpo"],
    "timelimit": ["time_limit = 600", "time_limit=600", "time_limit=600"],
    "presets": ["presets='best_quality'", "presets='best_quality'", "presets='best_quality'"],
    "hp-method": ["none", "problem_type = 'regression'", "tabular autogluon"],
    "score": ["1.39373", "0.46870", "0.49696"]
})
Out[65]:
model timelimit presets hp-method score
0 initial time_limit = 600 presets='best_quality' none 1.39373
1 add_features time_limit=600 presets='best_quality' problem_type = 'regression' 0.46870
2 hpo time_limit=600 presets='best_quality' tabular autogluon 0.49696
In [66]:
def plot_series(time, series, format="-", start=0, end=None, label=None):
    plt.plot(time[start:end], series[start:end], format, label=label)
    plt.xlabel("Time")
    plt.ylabel("Value")
    if label:
        plt.legend(fontsize=14)
    plt.grid(True)
In [67]:
sub_new = pd.read_csv('submission_new_features.csv')
In [ ]:
import matplotlib.pyplot as plt
series = train["count"].to_numpy()
time = train["datetime"].to_numpy()


plt.figure(figsize=(350, 50))
plot_series(time, series)
plt.title("Train Data time series graph")
#plot_series(time1, series1)
plt.show()
No description has been provided for this image
In [69]:
sub_new.loc[:, "datetime"] = pd.to_datetime(sub_new.loc[:, "datetime"])

series1 = sub_new["count"].to_numpy()
time1 = sub_new["datetime"].to_numpy()

plt.figure(figsize=(400, 50))
#plot_series(time, series)
plot_series(time1, series1)
plt.title("Test Data time series graph")
plt.show()
No description has been provided for this image
In [71]:
import xgboost as xgb
In [72]:
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')
In [75]:
train_df['datetime'] = pd.to_datetime(train['datetime'])
test_df['datetime'] = pd.to_datetime(test['datetime'])

train_df['year'] = train_df['datetime'].dt.year
train_df['month'] = train_df['datetime'].dt.month
train_df['day'] = train_df['datetime'].dt.day
train_df['hour'] = train_df['datetime'].dt.hour

test_df['year'] = test_df['datetime'].dt.year
test_df['month'] = test_df['datetime'].dt.month
test_df['day'] = test_df['datetime'].dt.day
test_df['hour'] = test_df['datetime'].dt.hour
In [76]:
trainxgb = train_df.drop(['casual', 'registered','count', 'datetime'], axis=1)
trainxgb.head()
Out[76]:
season holiday workingday weather temp atemp humidity windspeed year month day hour
0 1 0 0 1 9.84 14.395 81 0.0 2011 1 1 0
1 1 0 0 1 9.02 13.635 80 0.0 2011 1 1 1
2 1 0 0 1 9.02 13.635 80 0.0 2011 1 1 2
3 1 0 0 1 9.84 14.395 75 0.0 2011 1 1 3
4 1 0 0 1 9.84 14.395 75 0.0 2011 1 1 4
In [77]:
countxgb = train_df['count']
countxgb.head()
Out[77]:
0    16
1    40
2    32
3    13
4     1
Name: count, dtype: int64
In [78]:
train_xgb = xgb.DMatrix(
    trainxgb, countxgb
)

params = {"objective": "reg:linear"} 
bst = xgb.train(params, train_xgb)

bst.predict(train_xgb)
[04:08:41] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost-split_1700181168148/work/src/objective/regression_obj.cu:213: reg:linear is now deprecated in favor of reg:squarederror.
Out[78]:
array([ 35.479843,  31.946043,  25.44875 , ..., 185.26509 , 141.68748 ,
       111.29078 ], dtype=float32)
In [79]:
!jupyter nbconvert --to html pro.ipynb
[NbConvertApp] WARNING | pattern 'bike_sharing.ipynb' matched no files
This application is used to convert notebook files (*.ipynb)
        to various other formats.

        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.

Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
    <cmd> --help-all

--debug
    set log level to logging.DEBUG (maximize logging output)
    Equivalent to: [--Application.log_level=10]
--show-config
    Show the application's configuration (human-readable format)
    Equivalent to: [--Application.show_config=True]
--show-config-json
    Show the application's configuration (json format)
    Equivalent to: [--Application.show_config_json=True]
--generate-config
    generate default config file
    Equivalent to: [--JupyterApp.generate_config=True]
-y
    Answer yes to any questions instead of prompting.
    Equivalent to: [--JupyterApp.answer_yes=True]
--execute
    Execute the notebook prior to export.
    Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
    Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
    Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
    Write notebook output to stdout instead of files.
    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
    Run nbconvert in place, overwriting the existing notebook (only
            relevant when converting to notebook format)
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
    Clear output of current file and save in place,
            overwriting the existing notebook.
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--coalesce-streams
    Coalesce consecutive stdout and stderr outputs into one stream (within each cell).
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --CoalesceStreamsPreprocessor.enabled=True]
--no-prompt
    Exclude input and output prompts from converted document.
    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
    Exclude input cells and output prompts from converted document.
            This mode is ideal for generating code-free reports.
    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
    Whether to allow downloading chromium if no suitable version is found on the system.
    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
    Disable chromium security sandbox when converting to PDF..
    Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
    Shows code input. This flag is only useful for dejavu users.
    Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
    Equivalent to: [--HTMLExporter.embed_images=True]
--sanitize-html
    Whether the HTML in Markdown cells and cell outputs should be sanitized..
    Equivalent to: [--HTMLExporter.sanitize_html=True]
--log-level=<Enum>
    Set the log level by value or name.
    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
    Default: 30
    Equivalent to: [--Application.log_level]
--config=<Unicode>
    Full path of a config file.
    Default: ''
    Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
    The export format to be used, either one of the built-in formats
            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf']
            or a dotted object name that represents the import path for an
            ``Exporter`` class
    Default: ''
    Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
    Name of the template to use
    Default: ''
    Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
    Name of the template file to use
    Default: None
    Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
    as prebuilt extension for the lab template)
    Default: 'light'
    Equivalent to: [--HTMLExporter.theme]
--sanitize_html=<Bool>
    Whether the HTML in Markdown cells and cell outputs should be sanitized.This
    should be set to True by nbviewer or similar tools.
    Default: False
    Equivalent to: [--HTMLExporter.sanitize_html]
--writer=<DottedObjectName>
    Writer class used to write the
                                        results of the conversion
    Default: 'FilesWriter'
    Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
    PostProcessor class used to write the
                                        results of the conversion
    Default: ''
    Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
    Overwrite base name use for output files.
                Supports pattern replacements '{notebook_name}'.
    Default: '{notebook_name}'
    Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
    Directory to write output(s) to. Defaults
                                  to output to the directory of each notebook. To recover
                                  previous default behaviour (outputting to the current
                                  working directory) use . as the flag value.
    Default: ''
    Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
    The URL prefix for reveal.js (version 3.x).
            This defaults to the reveal CDN, but can be any url pointing to a copy
            of reveal.js.
            For speaker notes to work, this must be a relative path to a local
            copy of reveal.js: e.g., "reveal.js".
            If a relative path is given, it must be a subdirectory of the
            current directory (from which the server is run).
            See the usage documentation
            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
            for more details.
    Default: ''
    Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
    The nbformat version to write.
            Use this to downgrade notebooks.
    Choices: any of [1, 2, 3, 4]
    Default: 4
    Equivalent to: [--NotebookExporter.nbformat_version]

Examples
--------

    The simplest way to use nbconvert is

            > jupyter nbconvert mynotebook.ipynb --to html

            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf'].

            > jupyter nbconvert --to latex mynotebook.ipynb

            Both HTML and LaTeX support multiple output templates. LaTeX includes
            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and
            'classic'. You can specify the flavor of the format used.

            > jupyter nbconvert --to html --template lab mynotebook.ipynb

            You can also pipe the output to stdout, rather than a file

            > jupyter nbconvert mynotebook.ipynb --stdout

            PDF is generated via latex

            > jupyter nbconvert mynotebook.ipynb --to pdf

            You can get (and serve) a Reveal.js-powered slideshow

            > jupyter nbconvert myslides.ipynb --to slides --post serve

            Multiple notebooks can be given at the command line in a couple of
            different ways:

            > jupyter nbconvert notebook*.ipynb
            > jupyter nbconvert notebook1.ipynb notebook2.ipynb

            or you can specify the notebooks list in a config file, containing::

                c.NbConvertApp.notebooks = ["my_notebook.ipynb"]

            > jupyter nbconvert --config mycfg.py

To see all available configurables, use `--help-all`.

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